I’ve seen Jennifer Hill and Ed George give great talks on Bayesian additive regression trees. It looked awesome. So why haven’t these papers appeared anywhere? All I can find are preprints.
I’ve seen Jennifer Hill and Ed George give great talks on Bayesian additive regression trees. It looked awesome. So why haven’t these papers appeared anywhere? All I can find are preprints.
Been wondering about the myself. But Robert Gramacy's extension to Treed Gaussian Processes is forthcoming in JASA.
One of the top papers on your list — about 4th or 5th down — appeared in JMR a couple of years ago.
A Direct Approach to Data Fusion – all 16 versions »
ZVI GILULA, RE MCCULLOCH, PE ROSSI – papers.ssrn.com
Page 1. A Direct Approach to Data Fusion Zvi Gilula Department of Statistics
Hebrew University Robert E. McCulloch Peter E. Rossi
appears in
Journal of Marketing Research, vol 43, Feb 2006
It's a marketing article, and JMR is a top journal in this field.
The paper below, third on your list, was published in the Journal of Marketing Research (a top marketing journal) in February, 2006.
A Direct Approach to Data Fusion – all 16 versions »
ZVI GILULA, RE MCCULLOCH, PE ROSSI – papers.ssrn.com
Page 1. A Direct Approach to Data Fusion Zvi Gilula Department of Statistics
Hebrew University Robert E. McCulloch Peter E. Rossi …
Maybe is the language they use. It is only intelligible to motivated Bayesians. A more accessible version is needed than this:
"Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms in particular, BART is defined by a statistical model: a prior and a likelihood."
Marketing of one's research is very important, they ain't doing themselves any favors.
I find a piece of research to be really good when it can be very impressive in the plainest of languages.
ArXiv.org has a copy of the BART paper:
http://arxiv.org/pdf/0806.3286
They are pretty good marketers actually; I have been to one of their talks, hosted by Andrew. I pointed a friend to this method; apparently, it's missing a predict method.
An application paper in a marketing journal is fine, and Arxiv is fine also, but I'm surprised not to see a paper describing the key ides of the method in a statistics journal.
It appears that the package is not only lacking a predict method, it also doesn't handle the normal formula + data.frame interface. The input and output are pretty simple so it shouldn't take a whole lot of wrapping to remedy that.
Anybody who really wants to try it out should be able to get on with it pretty easily.